/calculate/

Finding people in their moment of need

What pages did we generate?

We generated around 2,000 pages, all of the form How to Calculate {metric} in {platform}, where {metric} corresponds to a list of common financial metrics, and {platform} corresponds to a list of the accounting platforms that Causal integrates with. We had about 100 of the former, and 20 of the latter, giving us roughly 2,000 pages overall.

This is a classic example of a 2-dimensional, structured campaign. We had two variables ({metric} and {platform} that we were iterating through, and a finite number of values for each.

You can see an example page here.

Why did we do this?

I've already explored why we did this project on the high-level strategy page, but in short:

  • One big USP of Causal is its ability to integrate with accounting platforms, and calculate complex financial metrics on top of the data from those platforms.

  • We therefore thought that users searching for how to calculate metrics in these platforms would be an ideal fit for Causal.

  • Additionally, the scale of this project meant we'd be going after lots of low-competition terms. Many of the terms would be so low-competition, that it'd be easy for us to rank highly just by virtue of being one of the only relevant results.

Statistics

Pages Generated

2,000

Total Sessions

40k

Sessions per Month

4k

Position #1 Keywords

48

Notes

  • In the example further up the page, you might notice that the content looks a little different. This project in fact used a modified/cut-down version of Byword, just to generate a little bit of content about the metric in question. The rest of the article is largely hard-coded, pulling in the metric name every now and then.

    • This was a design choice based on the fact that it's generally not possible to calculate most of these metrics inside of the platforms (meaning there's not much we can really write on the topic itself), and so we wanted to steer users towards Causal as a solution. Byword isn't great at writing about products like Causal, which is why we hardcoded a lot of the text.

  • If you're particularly interested in this campaign, I actually have an older case study on it too.

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